Computer-implemented visual object classification, also called object recognition, pertains to classifying visual representations of real-life objects found in still images or motion videos captured by a camera. By performing visual object classification, each visual object found in the still images or motion video is classified according to its type (such as, for example, human, vehicle, and animal).
Surveillance systems typically employ video cameras or other image capturing devices or sensors to collect image data such as videos. In the simplest systems, images represented by the image data are displayed for contemporaneous screening by security personnel and/or recorded for later review after a security breach. In those systems, the task of detecting and classifying visual objects-of-interest is performed by a human observer. A significant advance occurs when the system itself is able to perform object detection and classification, either partly or completely.
In a typical surveillance system, one may be interested in, for example, detecting objects such as humans, vehicles, and animals that move through the environment. More generally, it is beneficial for a surveillance system to be able to, without relying on assistance from a human operator, identify and classify, in a computationally efficiently manner, different objects that are recorded by the cameras that form part of the system. It may also be beneficial for a surveillance system to be able to monitor objects as they move in a region.